DS004448#

The BMI-HDEEG dataset 4

Access recordings and metadata through EEGDash.

Citation: Seitaro Iwama, Masumi Morishige, Yoshikazu Takahashi, Ryotaro Hirose, Midori Kodama, Junichi Ushiba (2023). The BMI-HDEEG dataset 4. 10.18112/openneuro.ds004448.v1.0.2

Modality: eeg Subjects: 56 Recordings: 1407 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004448

dataset = DS004448(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004448(cache_dir="./data", subject="01")

Advanced query

dataset = DS004448(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004448,
  title = {The BMI-HDEEG dataset 4},
  author = {Seitaro Iwama and Masumi Morishige and Yoshikazu Takahashi and Ryotaro Hirose and Midori Kodama and Junichi Ushiba},
  doi = {10.18112/openneuro.ds004448.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004448.v1.0.2},
}

About This Dataset#

Data Descriptor Article

Iwama, S., Morishige, M., Kodama, M. et al. High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing. Sci Data 10, 385 (2023). https://doi.org/10.1038/s41597-023-02260-6

Sample code

Junichi-Ushiba-Laboratory/pj-hd-smrbmi

Dataset Information#

Dataset ID

DS004448

Title

The BMI-HDEEG dataset 4

Year

2023

Authors

Seitaro Iwama, Masumi Morishige, Yoshikazu Takahashi, Ryotaro Hirose, Midori Kodama, Junichi Ushiba

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004448.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004448,
  title = {The BMI-HDEEG dataset 4},
  author = {Seitaro Iwama and Masumi Morishige and Yoshikazu Takahashi and Ryotaro Hirose and Midori Kodama and Junichi Ushiba},
  doi = {10.18112/openneuro.ds004448.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004448.v1.0.2},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 56

  • Recordings: 1407

  • Tasks: 1

Channels & sampling rate

  • Channels: 129

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 38.2 GB

  • File count: 1407

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004448.v1.0.2

Provenance

API Reference#

Use the DS004448 class to access this dataset programmatically.

class eegdash.dataset.DS004448(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004448. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 56; recordings: 280; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004448 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004448

Examples

>>> from eegdash.dataset import DS004448
>>> dataset = DS004448(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#